Analysis of Multi-Dimensional Road Accident Data for Disaster Management in Smart Cities
Bibliographic record
Abstract
In the current data-driven era, large volumes of data of different dimensions are generated and collected at a rapid rate. Examples of these big data include transportation data (e.g., traffic accident data). Integration of different transportation data, as well as reuse of past knowledge and information on public transit, can be for social good (e.g., can help road users avoid traffic accidents). Multi-dimensional data analysis and mining helps reveal factors associating with, or contributing to, traffic accidents. To manage this type of human-made disaster, we present in this paper a data science solution for multi-dimensional analysis of traffic accident data. It integrates heterogeneous data regarding vehicles, accidents and causality. It reuses past knowledge and information discovered from historical data for handling future situations. Evaluation on real-life accident data from the UK reveals some common conditions leading to serious and/or fatal accidents. It demonstrates the practicality of our solution in multi-dimensional analysis of traffic accident data, as well as the benefits of data integration and information (and knowledge) reuse, for disaster management in smart cities. Moreover, it is important to note that, although we illustrate our solution on UK accident data, our solution is expected to be reusable for the analysis of traffic accidents, support of disaster management, and building of smart cities at other locations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".